PIDN replaces repeated multi-noise ZNE evaluations with a trained network that denoises expectation values and gradients from noisy data plus history, achieving comparable optimization on quantum models with 4-6x fewer circuits.
A deep learning model for noise prediction on near-term quantum devices
3 Pith papers cite this work. Polarity classification is still indexing.
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Genetic algorithm-optimized dynamical decoupling sequences empirically outperform canonical DD sequences on IBM quantum processors for circuits up to 100 qubits, with gains increasing with size and complexity.
Tuning gate durations in noisy transmon qubit rings raises fidelity, with local maxima appearing under strong noise and an ML model predicting optima for new hardware.
citing papers explorer
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Accelerating Noisy Variational Quantum Algorithms with Physics-Informed Denoising Networks
PIDN replaces repeated multi-noise ZNE evaluations with a trained network that denoises expectation values and gradients from noisy data plus history, achieving comparable optimization on quantum models with 4-6x fewer circuits.
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Empirical learning of dynamical decoupling on quantum processors
Genetic algorithm-optimized dynamical decoupling sequences empirically outperform canonical DD sequences on IBM quantum processors for circuits up to 100 qubits, with gains increasing with size and complexity.
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Enhancing Circuit Fidelity in Transmon Qubit Rings via Operation Duration Tuning under Strong Connectivity Noise
Tuning gate durations in noisy transmon qubit rings raises fidelity, with local maxima appearing under strong noise and an ML model predicting optima for new hardware.